# -*- coding:utf-8 -*-
"""
Created on 2016-12-22
@author: MG
"""
import os
import time
import functools
from datetime import datetime, date, timedelta
import pytz
import numpy as np
# from pandas.tslib import Timestamp
from pandas import Timestamp
import re
import pandas as pd
from collections import OrderedDict
import logging
import warnings
from functools import reduce
import xlrd

STR_FORMAT_DATE = '%Y-%m-%d'
STR_FORMAT_DATETIME = '%Y-%m-%d %H:%M:%S'
STR_FORMAT_DATETIME2 = '%Y-%m-%d %H:%M:%S.%f'
PATTERN_DATE_FORMAT_RESTRICT = re.compile(r"\d{4}(\D)*\d{2}(\D)*\d{2}")
PATTERN_DATE_FORMAT = re.compile(r"\d{4}(\D)*\d{1,2}(\D)*\d{1,2}")


def populate_obj(model_obj, data_dic: dict, attr_list=None, error_if_no_key=False):
    """
    通过 dict 设置模型对应的属性
    :param model_obj:
    :param data_dic:
    :param attr_list:
    :param error_if_no_key:
    :return:
    """
    for name in (attr_list if attr_list is not None else data_dic.keys()):
        if name in data_dic:
            setattr(model_obj, name, data_dic[name])
        elif error_if_no_key:
            raise KeyError("data_dic 缺少 '%s' key 无法设置到 %s" % (name, model_obj.__class__.__name__))
        else:
            warnings.warn("data_dic 缺少 '%s' key 无法设置到 %s" % (name, model_obj.__class__.__name__))


def try_n_times(times=3, sleep_time=3, logger: logging.Logger=None):
    """
    尝试最多 times 次，异常捕获记录后继续尝试
    :param times:
    :param sleep_time:
    :param logger: 如果异常需要 log 记录则传入参数
    :return:
    """
    last_invoked_time = [None]

    def wrap_func(func):

        @functools.wraps(func)
        def try_it(*arg, **kwargs):
            for n in range(1, times+1):
                if sleep_time > 0 and last_invoked_time[0] is not None\
                        and (time.time() - last_invoked_time[0]) < sleep_time:
                    time.sleep(sleep_time - (time.time() - last_invoked_time[0]))

                try:
                    ret_data = func(*arg, **kwargs)
                except:
                    if logger is not None:
                        logger.exception("第 %d 次调用 %s(%s, %s) 出错", n, func.__name__, arg, kwargs)
                    continue
                finally:
                    last_invoked_time[0] = time.time()

                break
            else:
                ret_data = None

            return ret_data

        return try_it

    return wrap_func


def date_2_str(dt):
    if dt is not None and type(dt) in (date, datetime, Timestamp):
        dt_str = dt.strftime(STR_FORMAT_DATE)
    else:
        dt_str = dt
    return dt_str


def datetime_2_str(dt, format=STR_FORMAT_DATETIME):
    if dt is not None and type(dt) in (date, datetime, Timestamp):
        dt_str = dt.strftime(format)
    else:
        dt_str = dt
    return dt_str


def str_2_bytes(input_str):
    """
    用于将 str 类型转换为 bytes 类型
    :param input_str:
    :return:
    """
    return input_str.encode(encoding='GBK')


def bytes_2_str(bytes_str):
    """
    用于将bytes 类型转换为 str 类型
    :param bytes_str:
    :return:
    """
    return str(bytes_str, encoding='GBK')


def timedelta_2_str(td):
    """
    用于将 pd.Timedelta 类型转换为 str 类型
    :param td:
    :return:
    """
    if isinstance(td, pd.Timedelta):
        ret = str(td).split()[-1]
    else:
        ret = td
    return ret


def pattern_data_format(data_str):
    """
    识别日期格式（例如：2017-12-23），并将其翻译成 %Y-%m-%d 类似的格式
    :param data_str:
    :return:
    """
    date_str_format = PATTERN_DATE_FORMAT_RESTRICT.sub(r'%Y\1%m\2%d', data_str)
    if date_str_format == data_str:
        date_str_format = PATTERN_DATE_FORMAT.sub(r'%Y\1%m\2%d', data_str)
    return date_str_format


def try_2_date(something):
    """
    兼容各种格式尝试将 未知对象转换为 date 类型，相对比 str_2_date 消耗资源，支持更多的类型检查，字符串格式匹配
    :param something:
    :return:
    """
    if something is None:
        date_ret = something
    else:
        something_type = type(something)
        if something_type in (int, np.int64, np.int32, np.int16, np.int8):
            something = str(something)
            something_type = type(something)
        if type(something) == str:
            date_str_format = pattern_data_format(something)
            date_ret = datetime.strptime(something, date_str_format).date()
        elif type(something) in (Timestamp, datetime):
            date_ret = something.date()
        else:
            date_ret = something
    return date_ret


def pd_timedelta_2_timedelta(value):
    if isinstance(value, pd.Timedelta):
        # print(value, 'parse to timedelta')
        dt_value = timedelta(seconds=value.seconds)
    else:
        dt_value = value
    return dt_value


def get_first(iterable, func):
    for n in iterable:
        if func(n):
            return n
    return None


def get_first_idx(iterable, func):
    for idx, n in enumerate(iterable):
        if func(n):
            return idx
    return None


def get_last(iterable, func):
    count = len(iterable)
    for n in range(count - 1, -1, -1):
        if func(iterable[n]):
            return iterable[n]
    return None


def get_last_idx(iterable, func):
    """
    获取最后一个符合条件数据的数组索引
    :param iterable:
    :param func:
    :return:
    """
    count = len(iterable)
    for n in range(count - 1, -1, -1):
        if func(iterable[n]):
            return n
    return None


def replace_none_2_str(string, replace=''):
    return replace if string is None else string


def str_2_date(date_str, date_str_format=STR_FORMAT_DATE):
    """
    将日期字符串转换成 date 类型对象，如果字符串为 None 则返回None
    :param date_str: 日期字符串
    :param date_str_format: 日期字符串格式
    :return:
    """
    if date_str is not None:
        if type(date_str) == str:
            date_ret = datetime.strptime(date_str, date_str_format).date()
        elif type(date_str) in (Timestamp, datetime):
            date_ret = date_str.date()
        else:
            date_ret = date_str
    else:
        date_ret = date_str
    return date_ret


def date2datetime(dt):
    """
    date 类型转换问 datetime类型
    :param dt:
    :return:
    """
    return datetime(dt.year, dt.month, dt.day)


def clean_datetime_remove_time_data(atime):
    """
    将时间对象的 时、分、秒 全部清零
    :param atime:
    :return:
    """
    return datetime(atime.year, atime.month, atime.day)


def clean_datetime_remove_ms(atime):
    """
    将时间对象的 毫秒 全部清零
    :param atime:
    :return:
    """
    return datetime(atime.year, atime.month, atime.day, atime.hour, atime.minute, atime.second)


def utc2local(utc):
    localtime = datetime.utcfromtimestamp(utc).replace(tzinfo=pytz.utc).astimezone(pytz.timezone('Asia/Shanghai'))
    return localtime


def get_cntr_kind_name(contract_name):
    left_idx = len(contract_name) - 1
    for num_str in '1234567890':
        idx = contract_name.find(num_str, 0, left_idx)
        if idx == -1:
            continue
        if idx < left_idx:
            left_idx = idx
        if left_idx <= 1:
            break
    # print(lidx, contractname[:lidx])
    return contract_name[:left_idx]


CACHE_FOLDER_PATH_DIC = {}


def get_folder_path(target_folder_name=None, create_if_not_found=True):
    """
    获得系统缓存目录路径
    :param target_folder_name: 缓存目录名称
    :param create_if_not_found: 如果不存在则创建一个目录，默认：True
    :return: 缓存路径
    """
    global CACHE_FOLDER_PATH_DIC
    if target_folder_name is None:
        target_folder_name = 'cache'
    if target_folder_name not in CACHE_FOLDER_PATH_DIC:
        cache_folder_path_tmp = None
        print(u'查找数据目录path:', end="")
        parent_folder_path = os.path.abspath(os.curdir)
        par_path = parent_folder_path
        while not os.path.ismount(par_path):
            # print 'parent path = %s'%par_path
            dir_list = os.listdir(par_path)
            for dir_name in dir_list:
                # print d # .strip()
                if dir_name == target_folder_name:
                    cache_folder_path_tmp = os.path.join(par_path, dir_name)
                    print('<', cache_folder_path_tmp, '>')
                    break
            if cache_folder_path_tmp is not None:
                break
            par_path = os.path.abspath(os.path.join(par_path, os.path.pardir))
        if cache_folder_path_tmp is None:
            if create_if_not_found:
                cache_folder_path_tmp = os.path.abspath(os.path.join(parent_folder_path, target_folder_name))
                print('<', cache_folder_path_tmp, '> 创建缓存目录')
                os.makedirs(cache_folder_path_tmp)
                CACHE_FOLDER_PATH_DIC[target_folder_name] = cache_folder_path_tmp
        else:
            CACHE_FOLDER_PATH_DIC[target_folder_name] = cache_folder_path_tmp
    return CACHE_FOLDER_PATH_DIC.setdefault(target_folder_name, None)


def get_cache_file_path(cache_folder_name, file_name, create_if_not_found=True):
    """
    返回缓存文件的路径
    :param file_name: 缓存文件名称
    :param cache_folder_name: 缓存folder名称
    :param create_if_not_found: 如果不存在则创建一个目录，默认：True
    :return: 缓存文件路径
    """
    cache_folder_path = get_folder_path(cache_folder_name, create_if_not_found)
    return os.path.join(cache_folder_path, file_name)


def get_df_between_date(data_df, date_frm, date_to):
    """
    该函数仅用于 return_risk_analysis 中计算使用
    :param data_df:
    :param date_frm:
    :param date_to:
    :return:
    """
    if date_frm is not None and date_to is not None:
        new_data_df = data_df[(data_df.Date >= date_frm) & (data_df.Date <= date_to)]
    elif date_frm is not None:
        new_data_df = data_df[data_df.Date >= date_frm]
    elif date_to is not None:
        new_data_df = data_df[data_df.Date <= date_to]
    else:
        new_data_df = data_df
    new_data_df = new_data_df.reset_index(drop=True)
    return new_data_df


def return_risk_analysis_old(nav_df: pd.DataFrame, date_frm=None, date_to=None, freq='weekly', rf=0.02):
    """
    按列统计 rr_df 收益率绩效
    :param nav_df: 收益率DataFrame，index为日期，每一列为一个产品的净值走势
    :param date_frm: 统计日期区间，可以为空
    :param date_to: 统计日期区间，可以为空
    :param freq: None 自动识别, 'daily' 'weekly' 'monthly'
    :param rf: 无风险收益率，默认 0.02
    :return:
    """
    nav_df.index = [try_2_date(idx) for idx in nav_df.index]
    nav_sorted_df = nav_df.sort_index()
    rr_df = (1 + nav_sorted_df.pct_change().fillna(0)).cumprod()
    rr_df.index = [try_2_date(d) for d in rr_df.index]
    # 计算数据实际频率是日频、周频、月頻
    rr_df_len = rr_df.shape[0]
    day_per_data = (rr_df.index[rr_df_len - 1] - rr_df.index[0]).days / rr_df_len
    if day_per_data <= 0.005:
        freq_real = 'minute'
    elif day_per_data <= 0.2:
            freq_real = 'hour'
    elif day_per_data <= 2:
        freq_real = 'daily'
    elif day_per_data <= 10:
        freq_real = 'weekly'
    else:
        freq_real = 'monthly'
    if freq is None:
        freq = freq_real
    elif freq != freq_real:
        warnings_msg = "data freq wrong, expect %s, but %s was detected" % (freq, freq_real)
        # warnings.warn(warnings_msg)
        # logging.warning(warnings_msg)
        raise ValueError(warnings_msg)

    freq_str = ''
    if freq == 'weekly':
        data_count_per_year = 50
        freq_str = '周'
    elif freq == 'monthly':
        data_count_per_year = 12
        freq_str = '月'
    elif freq == 'daily':
        data_count_per_year = 250
        freq_str = '日'
    elif freq == 'hour':
        data_count_per_year = 1250
        freq_str = '时'
    elif freq == 'minute':
        data_count_per_year = 75000
        freq_str = '分'
    else:
        raise ValueError('freq=%s 只接受 daily weekly monthly 三种之一', freq)
    stat_dic_dic = OrderedDict()
    # rr_df.index = [str_2_date(d) for d in rr_df.index]
    rr_uindex_df = rr_df.reset_index()
    col_name_list = list(rr_uindex_df.columns)
    date_col_name = col_name_list[0]
    col_name_list = col_name_list[1:]
    if type(date_frm) is str:
        date_frm = datetime.strptime(date_frm, '%Y-%m-%d').date()
    if type(date_to) is str:
        date_to = datetime.strptime(date_to, '%Y-%m-%d').date()
    for col_name in col_name_list:
        data_df = rr_uindex_df[[date_col_name, col_name]]
        # print(data_df)
        data_df.columns = ['Date', 'Value']
        data_df = get_df_between_date(data_df, date_frm, date_to)
        data_df.Value = data_df.Value / data_df.Value[0]
        data_df['ret'] = data_df.Value.pct_change().fillna(0)
        date_span = data_df.Date[data_df.index[-1]] - data_df.Date[data_df.index[0]]
        date_span_fraction = 365 / date_span.days if date_span.days > 0 else 1
        # basic indicators
        CAGR = data_df.Value[data_df.index[-1]] ** date_span_fraction - 1
        period_rr = data_df.Value[data_df.index[-1]] - 1
        ann_vol = np.std(data_df.ret, ddof=1) * np.sqrt(data_count_per_year)
        down_side_vol = np.std(data_df.ret[data_df.ret < 0], ddof=1) * np.sqrt(data_count_per_year)
        # WeeksNum = data.shape[0]
        profit_loss_ratio = -np.mean(data_df.ret[data_df.ret > 0]) / np.mean(data_df.ret[data_df.ret < 0])
        win_ratio = len(data_df.ret[data_df.ret >= 0]) / len(data_df.ret)
        min_value = min(data_df.Value)
        final_value = data_df.Value[data_df.index[-1]]
        max_ret = max(data_df.ret)
        min_ret = min(data_df.ret)
        # End of basic indicators
        # max dropdown related
        data_df['mdd'] = data_df.Value / data_df.Value.cummax() - 1
        mdd_size = min(data_df.mdd)
        droparray = pd.Series(data_df.index[data_df.mdd == 0])
        if len(droparray) == 1:
            mdd_max_period = len(data_df.mdd)
        else:
            if float(data_df.Value[droparray.tail(1)]) > float(data_df.Value.tail(1)):
                droparray = droparray.append(pd.Series(data_df.index[-1]), ignore_index=True)
            mdd_max_period = max(droparray.diff().dropna()) - 1
        # End of max dropdown related
        # High level indicators
        sharpe_ratio = (CAGR - rf) / ann_vol
        sortino_ratio = (CAGR - rf) / down_side_vol
        calmar_ratio = CAGR / (-mdd_size)
        #  Natural month return
        j = 1
        for i in data_df.index:
            if i == 0:
                month_ret = pd.DataFrame([[data_df.Date[i], data_df.Value[i]]], columns=('Date', 'Value'))
            else:
                if data_df.Date[i].month != data_df.Date[i - 1].month:
                    month_ret.loc[j] = [data_df.Date[i - 1], data_df.Value[i - 1]]
                    j += 1
        month_ret.loc[j] = [data_df.Date[data_df.index[-1]], data_df.Value[data_df.index[-1]]]
        month_ret['ret'] = month_ret.Value.pct_change().fillna(0)
        max_rr_month = max(month_ret.ret)
        min_rr_month = min(month_ret.ret)
        # End of Natural month return
        data_len = data_df.shape[0]
        date_begin = data_df.Date[0]  # .date()
        date_end = data_df.Date[data_len-1]
        stat_dic = OrderedDict([('起始日期', date_begin),
                                ('截止日期', date_end),
                                ('区间收益率', '%.2f%%' % (period_rr * 100)),
                                ('最终净值', '%.4f' % final_value),
                                ('最低净值', '%.4f' % min_value),
                                ('年化收益率', '%.2f%%' % (CAGR * 100)),
                                ('年化波动率', '%.2f%%' % (ann_vol * 100)),
                                ('年化下行波动率', '%.2f%%' % (down_side_vol * 100)),
                                ('最大回撤', '%.2f%%' % (mdd_size * 100)),
                                ('夏普率', '%.2f' % sharpe_ratio),
                                ('索提诺比率', '%.2f' % sortino_ratio),
                                ('卡马比率', '%.2f' % calmar_ratio),
                                ('盈亏比', '%.2f' % profit_loss_ratio),
                                ('胜率', '%.2f' % win_ratio),
                                ('最长不创新高（%s）' % freq_str, mdd_max_period),
                                ('统计周期最大收益', '%.2f%%' % (max_ret * 100)),
                                ('统计周期最大亏损', '%.2f%%' % (min_ret * 100)),
                                ('最大月收益', '%.2f%%' % (max_rr_month * 100)),
                                ('最大月亏损', '%.2f%%' % (min_rr_month * 100))])
        stat_dic_dic[col_name] = stat_dic
    stat_df = pd.DataFrame(stat_dic_dic)
    stat_df = stat_df.ix[list(stat_dic.keys())]
    return stat_df


def return_risk_analysis(nav_df: pd.DataFrame, date_frm=None, date_to=None, freq='weekly', rf=0.02, suffix_name=None):
    """
    按列统计 rr_df 收益率绩效
    :param nav_df: 收益率DataFrame，index为日期，每一列为一个产品的净值走势
    :param date_frm: 统计日期区间，可以为空
    :param date_to: 统计日期区间，可以为空
    :param freq: None 自动识别, 'daily' 'weekly' 'monthly'
    :param rf: 无风险收益率，默认 0.02
    :return:
    """
    nav_sorted_df = nav_df.copy()
    nav_sorted_df.index = pd.to_datetime([try_2_date(idx) for idx in nav_sorted_df.index])
    nav_sorted_df.sort_index(inplace=True)
    # 计算数据实际频率是日频、周频、月頻
    data_count = nav_sorted_df.shape[0]
    day_per_data = (nav_sorted_df.index[data_count - 1] - nav_sorted_df.index[0]).days / data_count
    if day_per_data <= 0.008:
        freq_real = 'minute'
    elif day_per_data <= 0.2:
            freq_real = 'hour'
    elif day_per_data <= 2:
        freq_real = 'daily'
    elif day_per_data <= 10:
        freq_real = 'weekly'
    else:
        freq_real = 'monthly'
    if freq is None:
        freq = freq_real
    elif freq != freq_real:
        warnings_msg = "data freq wrong, expect %s, but %s was detected" % (freq, freq_real)
        # warnings.warn(warnings_msg)
        # logging.warning(warnings_msg)
        raise ValueError(warnings_msg)

    freq_str = ''
    if freq == 'weekly':
        data_count_per_year = 50
        freq_str = '周'
    elif freq == 'monthly':
        data_count_per_year = 12
        freq_str = '月'
    elif freq == 'daily':
        data_count_per_year = 250
        freq_str = '日'
    elif freq == 'hour':
        data_count_per_year = 1250
        freq_str = '时'
    elif freq == 'minute':
        data_count_per_year = 75000
        freq_str = '分'
    else:
        raise ValueError('freq=%s 只接受 daily weekly monthly 三种之一', freq)
    stat_dic_dic = OrderedDict()
    mon_rr_dic = {}
    if type(date_frm) is str:
        date_frm = datetime.strptime(date_frm, '%Y-%m-%d').date()
    if type(date_to) is str:
        date_to = datetime.strptime(date_to, '%Y-%m-%d').date()

    col_name_list = list(nav_sorted_df.columns)
    # date_col_name = col_name_list[0]
    # col_name_list = col_name_list[1:]
    for col_name in col_name_list:
        data_sub_df = nav_sorted_df[[col_name]].dropna()
        if data_sub_df.shape[0] == 0:
            continue
        rr_df = (1 + data_sub_df.pct_change().fillna(0)).cumprod()
        # rr_df.index = [try_2_date(d) for d in rr_df.index]
        data_df = rr_df.reset_index()
        data_df.columns = ['Date', 'Value']
        data_df = get_df_between_date(data_df, date_frm, date_to)
        data_df.Value = data_df.Value / data_df.Value[0]
        data_df['ret'] = data_df.Value.pct_change().fillna(0)
        date_span = data_df.Date[data_df.index[-1]] - data_df.Date[data_df.index[0]]
        date_span_fraction = 365 / date_span.days if date_span.days > 0 else 1
        # basic indicators
        CAGR = data_df.Value[data_df.index[-1]] ** date_span_fraction - 1
        period_rr = data_df.Value[data_df.index[-1]] - 1
        ann_vol = np.std(data_df.ret, ddof=1) * np.sqrt(data_count_per_year)
        down_side_vol = np.std(data_df.ret[data_df.ret < 0], ddof=1) * np.sqrt(data_count_per_year)
        # WeeksNum = data.shape[0]
        profit_loss_ratio = -np.mean(data_df.ret[data_df.ret > 0]) / np.mean(data_df.ret[data_df.ret < 0])
        win_ratio = len(data_df.ret[data_df.ret >= 0]) / len(data_df.ret)
        min_value = min(data_df.Value)
        final_value = data_df.Value[data_df.index[-1]]
        max_ret = max(data_df.ret)
        min_ret = min(data_df.ret)
        # End of basic indicators
        # max dropdown related
        data_df['mdd'] = data_df.Value / data_df.Value.cummax() - 1
        mdd_size = min(data_df.mdd)
        droparray = pd.Series(data_df.index[data_df.mdd == 0])
        if len(droparray) == 1:
            mdd_max_period = len(data_df.mdd)
        else:
            if float(data_df.Value[droparray.tail(1)]) > float(data_df.Value.tail(1)):
                droparray = droparray.append(pd.Series(data_df.index[-1]))  # , ignore_index=True
            mdd_max_period = max(droparray.diff().dropna()) - 1
        # End of max dropdown related
        # High level indicators
        sharpe_ratio = (CAGR - rf) / ann_vol
        sortino_ratio = (CAGR - rf) / down_side_vol
        calmar_ratio = CAGR / (-mdd_size)
        #  Natural month return
        j = 1
        for i in data_df.index:
            if i == 0:
                month_ret = pd.DataFrame([[data_df.Date[i], data_df.Value[i]]], columns=('Date', 'Value'))
            else:
                if data_df.Date[i].month != data_df.Date[i - 1].month:
                    month_ret.loc[j] = [data_df.Date[i - 1], data_df.Value[i - 1]]
                    j += 1
        month_ret.loc[j] = [data_df.Date[data_df.index[-1]], data_df.Value[data_df.index[-1]]]
        month_ret['ret'] = month_ret.Value.pct_change().fillna(0)
        max_rr_month = max(month_ret.ret)
        min_rr_month = min(month_ret.ret)
        # End of Natural month return
        data_len = data_df.shape[0]
        date_begin = data_df.Date[0]  # .date()
        date_end = data_df.Date[data_len-1]
        stat_dic = OrderedDict([('起始日期', date_begin),
                                ('截止日期', date_end),
                                ('区间收益率', '%.2f%%' % (period_rr * 100)),
                                ('最终净值', '%.4f' % final_value),
                                ('最低净值', '%.4f' % min_value),
                                ('年化收益率', '%.2f%%' % (CAGR * 100)),
                                ('年化波动率', '%.2f%%' % (ann_vol * 100)),
                                ('年化下行波动率', '%.2f%%' % (down_side_vol * 100)),
                                ('最大回撤', '%.2f%%' % (mdd_size * 100)),
                                ('夏普率', '%.2f' % sharpe_ratio),
                                ('索提诺比率', '%.2f' % sortino_ratio),
                                ('卡马比率', '%.2f' % calmar_ratio),
                                ('盈亏比', '%.2f' % profit_loss_ratio),
                                ('胜率', '%.2f' % win_ratio),
                                ('最长不创新高（%s）' % freq_str, mdd_max_period),
                                ('统计周期最大收益', '%.2f%%' % (max_ret * 100)),
                                ('统计周期最大亏损', '%.2f%%' % (min_ret * 100)),
                                ('最大月收益', '%.2f%%' % (max_rr_month * 100)),
                                ('最大月亏损', '%.2f%%' % (min_rr_month * 100))])
        stat_dic_dic[col_name if suffix_name is None else col_name + "_" + suffix_name] = stat_dic

        # 按时间周期进行相关统计
        data_df = data_df.set_index('Date')[['Value']]
        # data_df_g = data_df.groupby(pd.Grouper(freq='M'))
        # TODO: 首月收益未被计算进去，以后再修复
        monthly_rr_df = data_df.resample('M', convention='end').last().pct_change().fillna(0)
        mon_rr_dic[col_name if suffix_name is None else col_name + "_" + suffix_name] = monthly_rr_df

    if len(stat_dic_dic) > 0:
        stat_df = pd.DataFrame(stat_dic_dic)
        stat_df = stat_df.loc[list(stat_dic.keys())]
    else:
        stat_df = None

    return stat_df, mon_rr_dic


class DataFrame(pd.DataFrame):
    def interpolate_inner(self, columns=None, inplace=False):
        if columns is None:
            columns = list(self.columns)
        data = self if inplace else self.copy()
        for col_name in columns:
            index_not_nan = data.index[~np.isnan(data[col_name])]
            if index_not_nan.shape[0] == 0:
                continue
            index_range = (min(index_not_nan), max(index_not_nan))
            # data[col_name][index_range[0]:index_range[1]].interpolate(inplace=True)
            data[col_name][index_range[0]:index_range[1]] = data[col_name][index_range[0]:index_range[1]].interpolate()
        # print(data)
        if ~inplace:
            return data

    def map(self, func):
        row_count, col_count = self.shape
        columns = list(self.columns)
        indexes = list(self.index)
        for col_num in range(col_count):
            col_val = columns[col_num]
            for row_num in range(row_count):
                row_val = indexes[row_num]
                data_val = self.iloc[row_num, col_num]
                self.iloc[row_num, col_num] = func(col_val, row_val, data_val)
        return self


def reduce_list(funx, data_list, initial=None):
    result_list = []
    def reduce_func(x, y):
        # print(x,y)
        result = funx(x, y)
        result_list.append(result)
        return result
    if initial is None:
        reduce(reduce_func, data_list)
    else:
        reduce(reduce_func, data_list, initial)
    return result_list


def _calc_mdd_4_drawback_analysis(pair, y):
    """
    此函数仅供 drawback_analysis 使用
    用于计算最大回撤使用
    :param pair:
    :param y:
    :return:
    """
    max_y_last = pair[0]
    max_y = max_y_last if max_y_last > y else y
    mdd_last = pair[1]
    keep_max = pair[2]
    dd = y / max_y -1
    if keep_max:
        mdd = dd if dd < mdd_last else mdd_last
    else:
        mdd = dd
    return max_y, mdd, keep_max


def drawback_analysis(data_df, keep_max=False):
    """
    计算给定 DataFrame 数据对应的时间序列最大回撤数据
    :param data_df:
    :return:
    """
    if data_df is None or data_df.shape[0] <= 1:
        mdd_df = None
    else:
        mdd_df = data_df.apply(lambda xx: [rr[1] for rr in reduce_list(_calc_mdd_4_drawback_analysis, xx, (xx.iloc[0], 0, keep_max))])
    return mdd_df


def return_risk_analysis_by_xls(file_path, date_col=None, nav_col_list=None, encoding=None):
    """
    读xls文件，对每个sheet进行分析，并最终合并绩效分析报告
    回撤分析分别生成文件显示
    :param file_path:
    :return:
    """
    file_path_no_extention, file_extension = os.path.splitext(file_path)
    if file_extension.lower() == '.csv':
        is_csv_file = True
    else:
        is_csv_file = False
    if is_csv_file:
        sheet_names = ['sheet1']
    else:
        workbook = xlrd.open_workbook(file_path)
        sheet_names = workbook.sheet_names()

    sheet_mdd_df_dic = {}
    stat_df = None
    sheet_mon_rr_dic = {}
    for sheet_name in sheet_names:
        # if sheet_name not in col_names:
        #     continue
        try:
            index_col = 0
            if isinstance(date_col, str):
                if is_csv_file:
                    raise ValueError('csv 文件不支持 index_col 参数为字符串')
                sheet = workbook.sheet_by_name(sheet_name)
                # 取得日期索引后退出
                col_name = sheet.cell_value(0, index_col)
                while not(col_name is None or col_name == ""):
                    if col_name == date_col:
                        break
                    index_col += 1
                    col_name = sheet.cell_value(0, index_col)
            elif isinstance(date_col, int):
                index_col = date_col
            else:
                index_col = 0
            # 默认第0列为日期
            # sheetname Deprecated since version 0.21.0: Use sheet_name instead
            if is_csv_file:
                data_df = pd.read_csv(file_path, index_col=index_col, encoding=encoding)  # 某些版本使用 sheet_name
            else:
                data_df = pd.read_excel(file_path, index_col=index_col, sheet_name=sheet_name)  # 某些版本使用 sheet_name

            if data_df is None or data_df.shape[0] == 0:
                continue
            if nav_col_list is not None:
                data_df = data_df[nav_col_list]
            # 是否带suffix
            if re.search("[S|s]heet", sheet_name) is None:
                suffix_name = sheet_name
            else:
                suffix_name = None
            stat_df_tmp, mon_rr_dic = return_risk_analysis(data_df, freq=None, suffix_name=suffix_name)  # , freq='daily'
            if stat_df is None:
                stat_df = stat_df_tmp
            else:
                stat_df = stat_df.merge(stat_df_tmp, how='outer', left_index=True, right_index=True)

            mdd_df = drawback_analysis(data_df)
            sheet_mdd_df_dic[sheet_name] = mdd_df
            sheet_mon_rr_dic[sheet_name] = mon_rr_dic
        except:
            logging.exception('处理 %s 时失败', sheet_name)
            continue
    return stat_df, sheet_mdd_df_dic, sheet_mon_rr_dic


def merge_nav(df_list, date_from=None):
    """
    合并 df_list 将净值进行合并
    :param df_list:
    :param date_from:
    :return:
    """
    nav_df = None
    for nav_tmp_df in df_list:
        if nav_df is None:
            nav_df = nav_tmp_df
        else:
            nav_df = nav_df.merge(nav_tmp_df, how='outer', right_index=True, left_index=True)
    # 净值拟合
    # def calc_mean(nav_s):
    #     nav_sub_s = nav_s.dropna()
    #     if nav_sub_s.shape[0] == 0:
    #         mean_val = np.nan
    #     else:
    #         mean_val = nav_sub_s.mean()
    #     return mean_val

    pct_df = nav_df.pct_change()
    pct_mean_s = pct_df.mean(axis=1).fillna(0) + 1
    # 进行日期过滤
    if date_from is not None:
        pct_mean_s = pct_mean_s[pct_mean_s.index >= str_2_date(date_from)]
    nav_merged_df = pd.DataFrame({"nav": pct_mean_s.cumprod()})
    stat_df, _ = return_risk_analysis(nav_merged_df, freq=None)
    stat_funds_df, _ = return_risk_analysis(nav_df, freq=None)
    stat_all_df = stat_df.merge(stat_funds_df, how='outer', right_index=True, left_index=True)
    return nav_merged_df, nav_df, stat_all_df


def merge_nav_from_file(file_list, date_from=None):
    """
    从excel或csv文件中读取历史净值数据，进行合并
    :param file_list:
    :param date_from:
    :return:
    """
    df_list = []
    error_dic = {}
    for file_info_dic in file_list:
        # 读取文件
        file_path = file_info_dic['file_path']
        file_path_no_extention, file_extension = os.path.splitext(file_path)
        try:
            if file_extension == '.csv':
                data_df = pd.read_csv(file_path)
            elif file_extension in ('.xls', '.xlsx'):
                data_df = pd.read_excel(file_path, index_col=0).reset_index()
            else:
                error_dic['file type'] = '不支持 %s 净值文件类型' % file_extension
        except:
            error_dic['file read'] = '文件内容读取失败'
            logging.exception('文件内容读取失败：%s', file_path_no_extention)
            continue
        # 设置索引
        if 'date_colum_name' in file_info_dic:
            date_colum_name = file_info_dic['date_colum_name']
            data_df.set_index(date_colum_name, inplace=True)
        else:
            date_colum_name = data_df.columns[0]
            data_df.set_index(date_colum_name, inplace=True)
        # 设置索引日期格式
        data_df.index = [try_2_date(x) for x in data_df.index]
        # 取nav数据
        if 'nav_colum_name_list' in file_info_dic:
            nav_colum_name_list = file_info_dic['nav_colum_name_list']
            if isinstance(nav_colum_name_list, list):
                nav_colum_name_dic = OrderedDict()
                for nav_column_name in nav_colum_name_list:
                    if isinstance(nav_column_name, str):
                        nav_colum_name_dic[nav_column_name] = nav_column_name
                    elif isinstance(nav_column_name, tuple):
                        nav_colum_name_dic[nav_column_name[0]] = nav_column_name[1]
                    else:
                        raise ValueError("%s 列名称无效" % nav_column_name)
                nav_df = data_df[list(nav_colum_name_dic.keys())].rename(columns=nav_colum_name_dic)
            else:
                nav_df = data_df[[nav_colum_name_list]]
        else:
            nav_df = data_df
        # 添加 df_list
        df_list.append(nav_df)
    # 合并
    nav_merged_df, nav_df, stat_df = merge_nav(df_list, date_from)
    return nav_merged_df, nav_df, stat_df


if __name__ == "__main__":
    logging.basicConfig(level=logging.DEBUG, format='%(asctime)s: %(levelname)s [%(name)s] %(message)s')
    from pandas.io.formats.excel import ExcelCell
    # 基金绩效分析
    file_path = r'd:\WSPych\fof_ams\Stage\periodic_task\analysis_cache\2016-6-1_2018-6-1\各策略指数走势_按机构.csv'
    file_path_no_extention, _ = os.path.splitext(file_path)
    stat_df, sheet_mdd_df_dic, sheet_mon_rr_dic = return_risk_analysis_by_xls(file_path, encoding='GBK')  # , date_col="日期", nav_col_list=['产品净值']
    if stat_df is not None:
        stat_df.to_csv('%s_绩效统计.csv' % file_path_no_extention, encoding='GBK')
    for sheet_name, mdd_df in sheet_mdd_df_dic.items():
        mdd_df.to_csv('%s_%s_最大回撤.csv' % (file_path_no_extention, sheet_name), encoding='GBK')
    if len(sheet_mon_rr_dic) > 0:
        xls_file_path = '%s_%s_月度收益.xls' % (file_path_no_extention, sheet_name)
        writer = pd.ExcelWriter(xls_file_path)
        try:
            for sheet_name, mon_rr_dic in sheet_mon_rr_dic.items():
                start_row = 1
                for name, monthly_rr_df in mon_rr_dic.items():
                    year_set = {trade_date.year for trade_date in monthly_rr_df.index}
                    monthly_rr_matrix_df = pd.DataFrame(index=year_set, columns=range(1, 13))
                    for trade_date, rr_s in monthly_rr_df.T.items():
                        monthly_rr_matrix_df.loc[trade_date.year, trade_date.month] = '%2.2f%%' % (rr_s[0] * 100)
                    # 写 excel
                    # sheet.write(start_row, 0, name)
                    writer.write_cells([ExcelCell(0, 0, name)], sheet_name, startrow=start_row - 1)
                    monthly_rr_matrix_df.to_excel(writer, sheet_name, startrow=start_row)
                    start_row += len(year_set) + 3
        finally:
            writer.close()

    # 基金净值合并
    # file_list = [
    #     {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\新萌\复华1号历史净值180105(1).xls"},
    #     {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\新萌\历史净值171017.xls",
    #      'date_colum_name': '净值日期', 'nav_colum_name_list': ['最新净值']},
    #     {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\新萌\新萌拟合后净值.xlsx",
    #      'date_colum_name': '日期', 'nav_colum_name_list': ['拟合后净值']},
    #     {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\新萌\投放产品历史净值.csv",
    #      'date_colum_name': 'nav_date', 'nav_colum_name_list': ['nav_acc']},
    # ]

    # file_list = [
    #     {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\诚盛投资\诚盛2期Z期净值20171229nav.xlsx",
    #      'date_colum_name': '估值基准', 'nav_colum_name_list': [('单位净值',"诚盛2期Z期净值")]},
    #     {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\诚盛投资\诚盛1期净值表.xlsx",
    #      'date_colum_name': '日期', 'nav_colum_name_list': ['诚盛1期净值']},
    # ]

    file_list = [
        {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\合晟\合晟产品历史净值.csv"},
        {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\展弘\展弘投放产品历史净值.xlsx"},
        {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\新萌\新萌合并业绩后净值.xlsx"},
        {'file_path': r"d:\Works\F复华投资\L路演、访谈、评估报告\思勰\思勰合并后净值 2018 - 03 - 09.xls"},
    ]
    #
    # file_list = [
    #     {'file_path': r'd:\Works\F复华投资\L路演、访谈、评估报告\思勰\思瑞二号周净值.xlsx',
    #      'date_colum_name': 'date', 'nav_colum_name_list': ['累计净值']},
    #     {'file_path': r'd:\Works\F复华投资\L路演、访谈、评估报告\思勰\2016.1-2016.10思勰净值.xlsx'},
    #     {'file_path': r'd:\Works\F复华投资\L路演、访谈、评估报告\思勰\思诚十二号周净值(1).xlsx',
    #      'date_colum_name': 'date', 'nav_colum_name_list': ['累计净值']},
    #     {'file_path': r'd:\Works\F复华投资\L路演、访谈、评估报告\思勰\SM2082-思瑞二号私募投资基金周净值(1).xls',
    #      'date_colum_name': '日期', 'nav_colum_name_list': ['累计净值']},
    # ]

    # nav_merged_df, nav_df, stat_df = merge_nav_from_file(file_list)
    # logging.info("\n%s", nav_merged_df)
    # logging.info("\n%s", nav_df)
    # logging.info("\n%s", stat_df)
    #
    # os.path.dirname(file_list[0]['file_path'])
    # folder_path = os.path.dirname(file_list[0]['file_path'])
    # file_name = "合并后净值.xls"
    # file_path = os.path.join(folder_path, file_name)
    # with pd.ExcelWriter(file_path) as writer:
    #     nav_merged_df.to_excel(writer, sheet_name="合并净值")
    #     nav_df.to_excel(writer, sheet_name="基金净值")
    #     stat_df.to_excel(writer, sheet_name="绩效统计")
    #     writer.save()
    # logging.info("输出文件：\n%s", file_path)
